The current depth estimation networks do not sufficiently extract spatial features from images in outdoor scenes， leading to issues such as object edge distortion， blurriness， and regional pseudo-shadows in the output depth maps. To address these problems， this paper proposed a multi-frame self-supervised monocular depth estimation model with multi-scale feature enhancement. Firstly， the model's encoder incorporated an activation module based on large kernel attention to enhance its ability to extract global spatial features from the input image， preserving more spatial context information. Simultaneously， a structural enhancement module was introduced that can discriminate important features across channel dimensions， enhancing the network's perception of the structural characteristics of the image. Finally， the decoder used a dynamic upsampling method instead of the traditional nearest interpolation upsampling method to restore detailed information， thereby optimizing the pseudo-shadow phenomenon in the depth map to some extent. Experimental results demonstrate that the depth estimation network proposed in this paper outperforms current mainstream algorithms in tests on the KITTI and CityScapes outdoor datasets， particularly achieving a prediction accuracy rate of 90.3% on the KITTI dataset. Visualization results also indicate that the depth maps generated by our network model have clearer and more precise edges， effectively improving the prediction accuracy of the depth estimation network.